Volume 10 Issue 5 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 10 · NO. 5 | September 2023

CONTENTS

IN THIS ISSUE

Special Section on Artificial Intelligence in Medical Imaging for Clinical Practice (9)

Physics of Medical Imaging (2)

Image Processing (3)

Computer-Aided Diagnosis (4)

Image-Guided Procedures, Robotic Interventions, and Modeling (1)

Image Perception, Observer Performance, and Technology Assessment (1)

Biomedical Applications in Molecular, Structural, and Functional Imaging (2)

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Special Section on Artificial Intelligence in Medical Imaging for Clinical Practice

Special Section Editorial: Artificial Intelligence for Medical Imaging in Clinical Practice

Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides

Journal of Medical Imaging, Vol. 10, Issue 05, 051801, (October 2023) https://doi.org/10.1117/1.JMI.10.5.051801

TOPICS: Artificial intelligence, Medical imaging, Clinical practice, Pathology, Lung, Imaging devices, Cardiovascular magnetic resonance imaging, Cancer detection, Breast cancer, 3D image enhancement

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Artificial intelligence and digital pathology: clinical promise and deployment considerations

Mark D. Zarella, David S. McClintock, Harsh Batra, Rama R. Gullapalli, Michael Valante, Vivian O. Tan, Shubham Dayal, Kei Shing Oh, Haydee Lara, Chris A. Garcia, Esther Abels

Journal of Medical Imaging, Vol. 10, Issue 05, 051802, (July 2023) https://doi.org/10.1117/1.JMI.10.5.051802

TOPICS: Artificial intelligence, Pathology, Evolutionary algorithms, Diagnostics, Data modeling, Process modeling, Tissues, Algorithm development, Medicine, Image analysis

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Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges

Nicholas Petrick, Weijie Chen, Jana Delfino, Brandon Gallas, Yanna Kang, Daniel Krainak, Berkman Sahiner, Ravi Samala

Journal of Medical Imaging, Vol. 10, Issue 05, 051804, (June 2023) https://doi.org/10.1117/1.JMI.10.5.051804

TOPICS: Medical imaging, Data modeling, Medical devices, Education and training, Imaging devices, Performance modeling, Instrument modeling, Machine learning, Design and modelling, Medical device development

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Integration and evaluation of chest X-ray artificial intelligence in clinical practice

Koon-Pong Wong, Suzanne Y. Homer, Sindy H. Wei, Nazanin Yaghmai, Oscar A. Estrada Paz, Timothy J. Young, Russell G. Buhr, Igor Barjaktarevic, Liza Shrestha, Morgan Daly, Jonathan Goldin, Dieter R. Enzmann, Matthew S. Brown

Journal of Medical Imaging, Vol. 10, Issue 05, 051805, (April 2023) https://doi.org/10.1117/1.JMI.10.5.051805

TOPICS: Artificial intelligence, Chest imaging, Clinical practice, Radiology, Picture Archiving and Communication System, Computing systems, Medicine, Imaging systems, Image segmentation, Evolutionary algorithms

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Predicting left/right lung volumes, thoracic cavity volume, and heart volume from subject demographics to improve lung transplant

Lucas Pu, Joseph Leader, Alaa Ali, Zihan Geng, David Wilson

Journal of Medical Imaging, Vol. 10, Issue 05, 051806, (April 2023) https://doi.org/10.1117/1.JMI.10.5.051806

TOPICS: Lung, Heart, Computed tomography, Image segmentation, Performance modeling, Chest, Anatomy, Machine learning, Modeling, Education and training

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Purpose

Lung transplantation is the standard treatment for end-stage lung diseases. A crucial factor affecting its success is size matching between the donor’s lungs and the recipient’s thorax. Computed tomography (CT) scans can accurately determine recipient’s lung size, but donor’s lung size is often unknown due to the absence of medical images. We aim to predict donor’s right/left/total lung volume, thoracic cavity, and heart volume from only subject demographics to improve the accuracy of size matching.

Approach

A cohort of 4610 subjects with chest CT scans and basic demographics (i.e., age, gender, race, smoking status, smoking history, weight, and height) was used in this study. The right and left lungs, thoracic cavity, and heart depicted on chest CT scans were automatically segmented using U-Net, and their volumes were computed. Eight machine learning models [i.e., random forest, multivariate linear regression, support vector machine, extreme gradient boosting (XGBoost), multilayer perceptron (MLP), decision tree, k-nearest neighbors, and Bayesian regression) were developed and used to predict the volume measures from subject demographics. The 10-fold cross-validation method was used to evaluate the performances of the prediction models. R-squared (R²), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as performance metrics.

Results

The MLP model demonstrated the best performance for predicting the thoracic cavity volume (R²: 0.628, MAE: 0.736 L, MAPE: 10.9%), right lung volume (R²: 0.501, MAE: 0.383 L, MAPE: 13.9%), and left lung volume (R²: 0.507, MAE: 0.365 L, MAPE: 15.2%), and the XGBoost model demonstrated the best performance for predicting the total lung volume (R²: 0.514, MAE: 0.728 L, MAPE: 14.0%) and heart volume (R²: 0.430, MAE: 0.075 L, MAPE: 13.9%).

Conclusions

Our results demonstrate the feasibility of predicting lung, heart, and thoracic cavity volumes from subject demographics with superior performance compared with available studies in predicting lung volumes.

Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection

Mercedes Riveira-Martin, Alejandro Rodríguez-Ruiz, Robert Martí, Margarita Chevalier

Journal of Medical Imaging, Vol. 10, Issue 05, 051807, (April 2023) https://doi.org/10.1117/1.JMI.10.5.051807

TOPICS: Artificial intelligence, Breast, Breast density, Mammography, Cancer detection, Breast cancer, Image quality, Intelligence systems, Cancer, Diagnostics

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Deep learning architecture for 3D image super-resolution of late gadolinium enhanced cardiac MRI

Roshan Reddy Upendra, Richard Simon, Cristian A. Linte

Journal of Medical Imaging, Vol. 10, Issue 05, 051808, (May 2023) https://doi.org/10.1117/1.JMI.10.5.051808

TOPICS: Image segmentation, Super resolution, 3D modeling, Education and training, 3D image processing, Data modeling, 3D image enhancement, Interpolation, Image resolution, Deep learning

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Purpose

High-resolution late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) volumes are difficult to acquire due to the limitations of the maximal breath-hold time achievable by the patient. This results in anisotropic 3D volumes of the heart with high in-plane resolution, but low-through-plane resolution. Thus, we propose a 3D convolutional neural network (CNN) approach to improve the through-plane resolution of the cardiac LGE-MRI volumes.

Approach

We present a 3D CNN-based framework with two branches: a super-resolution branch to learn the mapping between low-resolution and high-resolution LGE-MRI volumes, and a gradient branch that learns the mapping between the gradient map of low-resolution LGE-MRI volumes and the gradient map of high-resolution LGE-MRI volumes. The gradient branch provides structural guidance to the CNN-based super-resolution framework. To assess the performance of the proposed CNN-based framework, we train two CNN models with and without gradient guidance, namely, dense deep back-projection network (DBPN) and enhanced deep super-resolution network. We train and evaluate our method on the 2018 atrial segmentation challenge dataset. Additionally, we also evaluate these trained models on the left atrial and scar quantification and segmentation challenge 2022 dataset to assess their generalization ability. Finally, we investigate the effect of the proposed CNN-based super-resolution framework on the 3D segmentation of the left atrium (LA) from these cardiac LGE-MRI image volumes.

Results

Experimental results demonstrate that our proposed CNN method with gradient guidance consistently outperforms bicubic interpolation and the CNN models without gradient guidance. Furthermore, the segmentation results, evaluated using Dice score, obtained using the super-resolved images generated by our proposed method are superior to the segmentation results obtained using the images generated by bicubic interpolation (p < 0.01) and the CNN models without gradient guidance (p < 0.05).

Conclusion

The presented CNN-based super-resolution method with gradient guidance improves the through-plane resolution of the LGE-MRI volumes and the structure guidance provided by the gradient branch can be useful to aid the 3D segmentation of cardiac chambers, such as LA, from the 3D LGE-MRI images.

Batch-balanced focal loss: a hybrid solution to class imbalance in deep learning

Jatin Singh, Cameron Beeche, Zhiyi Shi, Oliver Beale, Boris Rosin, Joseph Leader, Jiantao Pu

Journal of Medical Imaging, Vol. 10, Issue 05, 051809, (June 2023) https://doi.org/10.1117/1.JMI.10.5.051809

TOPICS: Glaucoma, Data modeling, Binary data, Visualization, Education and training, RGB color model, Image classification, Statistical modeling, Performance modeling, Deep learning

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Fully automated pipeline for measurement of the thoracic aorta using joint segmentation and localization neural network

Sudeep Katakol, Timothy J. Baker, Zhangxing Bian, Yanglong Lu, Greg Spahlinger, Charles R. Hatt, Nicholas S. Burris

Journal of Medical Imaging, Vol. 10, Issue 05, 051810, (October 2023) https://doi.org/10.1117/1.JMI.10.5.051810

TOPICS: Aorta, Education and training, Image segmentation, Arteries, Computed tomography, Anatomy, Voxels, Standards development, Aneurysms, Algorithm development

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Physics of Medical Imaging

Imaging of excised cochleae by micro-CT: staining, liquid embedding, and image modalities

Jannis Justus Schaeper, M. Charles Liberman, Tim Salditt

Journal of Medical Imaging, Vol. 10, Issue 05, 053501, (September 2023) https://doi.org/10.1117/1.JMI.10.5.053501

TOPICS: Cochlea, Phase retrieval, Tomography, Laboratories, Animals, Voxels, Nervous system, Image quality, Contrast transfer function, Phase contrast

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Purpose

Assessing the complex three-dimensional (3D) structure of the cochlea is crucial to understanding the fundamental aspects of signal transduction in the inner ear and is a prerequisite for the development of novel cochlear implants. X-ray phase-contrast computed tomography offers destruction-free 3D imaging with little sample preparation, thus preserving the delicate structure of the cochlea. The use of heavy metal stains enables higher contrast and resolution and facilitates segmentation of the cochlea.

Approach

For μ-CT of small animal and human cochlea, we explore the heavy metal osmium tetroxide (OTO) as a radiocontrast agent and delineate laboratory μ-CT from synchrotron CT. We investigate how phase retrieval can be used to improve the image quality of the reconstructions, both for stained and unstained specimens.

Results

Image contrast for soft tissue in an aqueous solution is insufficient under the in-house conditions, whereas the OTO stain increases contrast for lipid-rich tissue components, such as the myelin sheaths in nervous tissue, enabling contrast-based rendering of the different components of the auditory nervous system. The overall morphology of the cochlea with the three scalae and membranes is very well represented. Further, the image quality of the reconstructions improves significantly when a phase retrieval scheme is used, which is also suitable for non-ideal laboratory μ-CT settings. With highly brilliant synchrotron radiation (SR), we achieve high contrast for unstained whole cochleae at the cellular level.

Conclusions

The OTO stain is suitable for 3D imaging of small animal and human cochlea with laboratory μ-CT, and relevant pathologies, such as a loss of sensory cells and neurons, can be visualized. With SR and optimized phase retrieval, the cellular level can be reached even for unstained samples in aqueous solution, as demonstrated by the high visibility of single hair cells and spiral ganglion neurons.

Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithms using in silico and physical phantom studies

Andrey Makeev, Stephen Glick

Journal of Medical Imaging, Vol. 10, Issue 05, 053502, (October 2023) https://doi.org/10.1117/1.JMI.10.5.053502

TOPICS: Education and training, Denoising, Mammography, Data modeling, Breast, Monte Carlo methods, Image processing, Surface plasmons, Image restoration, Digital mammography

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Image Processing

Kernel-weighted contribution: a method of visual attribution for 3D deep learning segmentation in medical imaging

Sean Mullan, Milan Sonka

Journal of Medical Imaging, Vol. 10, Issue 05, 054001, (September 2023) https://doi.org/10.1117/1.JMI.10.5.054001

TOPICS: Image segmentation, Data modeling, Convolution, Medical imaging, Visualization, 3D modeling, Education and training, Deep learning, Visual process modeling, Surgery

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Purpose

Explaining deep learning model decisions, especially those for medical image segmentation, is a critical step toward the understanding and validation that will enable these powerful tools to see more widespread adoption in healthcare. We introduce kernel-weighted contribution, a visual explanation method for three-dimensional medical image segmentation models that produces accurate and interpretable explanations. Unlike previous attribution methods, kernel-weighted contribution is explicitly designed for medical image segmentation models and assesses feature importance using the relative contribution of each considered activation map to the predicted segmentation.

Approach

We evaluate our method on a synthetic dataset that provides complete knowledge over input features and a comprehensive explanation quality metric using this ground truth. Our method and three other prevalent attribution methods were applied to five different model layer combinations to explain segmentation predictions for 100 test samples and compared using this metric.

Results

Kernel-weighted contribution produced superior explanations of obtained image segmentations when applied to both encoder and decoder sections of a trained model as compared to other layer combinations (p < 0.0005). In between-method comparisons, kernel-weighted contribution produced superior explanations compared with other methods using the same model layers in four of five experiments (p < 0.0005) and showed equivalently superior performance to GradCAM++ when only using non-transpose convolution layers of the model decoder (p = 0.008).

Conclusion

The reported method produced explanations of superior quality uniquely suited to fully utilize the specific architectural considerations present in image and especially medical image segmentation models. Both the synthetic dataset and implementation of our method are available to the research community.

Patch-wise 3D segmentation quality assessment combining reconstruction and regression networks

Fahim Ahmed Zaman, Tarun Kanti Roy, Milan Sonka, Xiaodong Wu

Journal of Medical Imaging, Vol. 10, Issue 05, 054002, (September 2023) https://doi.org/10.1117/1.JMI.10.5.054002

TOPICS: Image segmentation, Image restoration, Education and training, Convolution, Cartilage, Network architectures, Voxels, 3D image processing, Bone, Medical image reconstruction

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Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk

Andreas D. Lauritzen, My Catarina von Euler-Chelpin, Elsebeth Lynge, Ilse Vejborg, Mads Nielsen, Nico Karssemeijer, Martin Lillholm

Journal of Medical Imaging, Vol. 10, Issue 05, 054003, (September 2023) https://doi.org/10.1117/1.JMI.10.5.054003

TOPICS: Education and training, Data modeling, Mammography, Breast cancer, Cancer, Tumor growth modeling, Breast, Tunable filters, Instrument modeling, Cancer detection

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Computer-Aided Diagnosis

Bayesian uncertainty estimation for detection of long-tailed and unseen conditions in medical images

Mina Rezaei, Janne Näppi, Bernd Bischl, Hiroyuki Yoshida

Journal of Medical Imaging, Vol. 10, Issue 05, 054501, (October 2023) https://doi.org/10.1117/1.JMI.10.5.054501

TOPICS: Gallium nitride, Education and training, Image segmentation, Data modeling, Semantics, Machine learning, Performance modeling, Medical imaging, Deep learning, Statistical modeling

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Purpose

Deep supervised learning provides an effective approach for developing robust models for various computer-aided diagnosis tasks. However, there is often an underlying assumption that the frequencies of the samples between the different classes of the training dataset are either similar or balanced. In real-world medical data, the samples of positive classes often occur too infrequently to satisfy this assumption. Thus, there is an unmet need for deep-learning systems that can automatically identify and adapt to the real-world conditions of imbalanced data.

Approach

We propose a deep Bayesian ensemble learning framework to address the representation learning problem of long-tailed and out-of-distribution (OOD) samples when training from medical images. By estimating the relative uncertainties of the input data, our framework can adapt to imbalanced data for learning generalizable classifiers. We trained and tested our framework on four public medical imaging datasets with various imbalance ratios and imaging modalities across three different learning tasks: semantic medical image segmentation, OOD detection, and in-domain generalization. We compared the performance of our framework with those of state-of-the-art comparator methods.

Results

Our proposed framework outperformed the comparator models significantly across all performance metrics (pairwise t-test: p < 0.01) in the semantic segmentation of high-resolution CT and MR images as well as in the detection of OOD samples (p < 0.01), thereby showing significant improvement in handling the associated long-tailed data distribution. The results of the in-domain generalization also indicated that our framework can enhance the prediction of retinal glaucoma, contributing to clinical decision-making processes.

Conclusions

Training of the proposed deep Bayesian ensemble learning framework with dynamic Monte-Carlo dropout and a combination of losses yielded the best generalization to unseen samples from imbalanced medical imaging datasets across different learning tasks.

Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis

Jiwoong Jeong, Chieh-Ju Chao, Reza Arsanjani, Kihong Kim, Melissa Pelkey, Yi-Chieh Chen, Raheel Ramzan, Mohammad Elbahnasawy, Mohamed Sleem, Chadi Ayoub, Juan Maria Farina, Martha Grogan, Garvan Kane, Bhavik Patel, Jae Oh, Imon Banerjee

Journal of Medical Imaging, Vol. 10, Issue 05, 054502, (October 2023) https://doi.org/10.1117/1.JMI.10.5.054502

TOPICS: Data modeling, Education and training, RGB color model, Echocardiography, Performance modeling, Deep learning, Motion models, Ablation, 3D modeling, Image classification

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Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer

Juhun Lee, Tamerlan Mustafaev, Robert Nishikawa

Journal of Medical Imaging, Vol. 10, Issue 05, 054503, (October 2023) https://doi.org/10.1117/1.JMI.10.5.054503

TOPICS: Mammography, Molybdenum, Breast, Cancer, Gallium nitride, Cancer detection, Education and training, Computer simulations, Tissues, Image processing

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Purpose

Generative adversarial networks (GANs) can synthesize various feasible-looking images. We showed that a GAN, specifically a conditional GAN (CGAN), can simulate breast mammograms with normal, healthy appearances and can help detect mammographically-occult (MO) cancer. However, similar to other GANs, CGANs can suffer from various artifacts, e.g., checkerboard artifacts, that may impact the quality of the final synthesized image, as well as the performance of detecting MO cancer. We explored the types of GAN artifacts that exist in mammogram simulations and their effect on MO cancer detection.

Approach

We first trained a CGAN using digital mammograms (FFDMs) of 1366 women with normal/healthy breasts. Then, we tested the trained CGAN on an independent MO cancer dataset with 333 women with dense breasts (97 MO cancers). We trained a convolutional neural network (CNN) on the MO cancer dataset, in which real and simulated mammograms were fused, to identify women with MO cancer. Then, a radiologist who was independent of the development of the CGAN algorithms evaluated the entire MO cancer dataset to identify and annotate artifacts in the simulated mammograms.

Results

We found four artifact types, including checkerboard, breast boundary, nipple-areola complex, and black spots around calcification artifacts, with an overall incidence rate over 69% (the individual incident rate ranged from 9% to 53%) from both normal and MO cancer samples. We then evaluated their potential impact on MO cancer detection. Even though various artifacts existed in the simulated mammogram, we found that it still provided complementary information for MO cancer detection when it was combined with the real mammograms.

Conclusions

We found that artifacts were pervasive in the CGAN-simulated mammograms. However, they did not negatively affect our MO cancer detection algorithm; the simulated mammograms still provided complementary information for MO cancer detection when combined with real mammograms.

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays

Mohammad Yahyatabar, Philippe Jouvet, Farida Cheriet

Journal of Medical Imaging, Vol. 10, Issue 05, 054504, (October 2023) https://doi.org/10.1117/1.JMI.10.5.054504

TOPICS: Image segmentation, Lung, Chest imaging, Image classification, Data modeling, Education and training, Performance modeling, Machine learning, Pathology, Statistical modeling

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Purpose

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can cause a dramatic drop in blood oxygen levels due to widespread lung inflammation. Chest radiography is widely used as a primary modality to detect ARDS due to its crucial role in diagnosing the syndrome, and the x-ray images can be obtained promptly. However, despite the extensive literature on chest x-ray (CXR) image analysis, there is limited research on ARDS diagnosis due to the scarcity of ARDS-labeled datasets. Additionally, many machine learning-based approaches result in high performance in pulmonary disease diagnosis, but their decisions are often not easily interpretable, which can hinder their clinical acceptance. This work aims to develop a method for detecting signs of ARDS in CXR images that can be clinically interpretable.

Approach

To achieve this goal, an ARDS-labeled dataset of chest radiography images is gathered and annotated for training and evaluation of the proposed approach. The proposed deep classification-segmentation model, Dense-Ynet, provides an interpretable framework for automatically diagnosing ARDS in CXR images. The model takes advantage of lung segmentation in diagnosing ARDS. By definition, ARDS causes bilateral diffuse infiltrates throughout the lungs. To consider the local involvement of lung areas, each lung is divided into upper and lower halves, and our model classifies the resulting lung quadrants.

Results

The quadrant-based classification strategy yields the area under the receiver operating characteristic curve of 95.1% (95% CI 93.5 to 96.1), which allows for providing a reference for the model’s predictions. In terms of segmentation, the model accurately identifies lung regions in CXR images even when lung boundaries are unclear in abnormal images.

Conclusions

This study provides an interpretable decision system for diagnosing ARDS, by following the definition used by clinicians for the diagnosis of ARDS from CXR images.

Image-Guided Procedures, Robotic Interventions, and Modeling

Evaluation of synthetically generated computed tomography for use in transcranial focused ultrasound procedures

Han Liu, Michelle Sigona, Thomas J. Manuel, Li Min Chen, Benoit M. Dawant, Charles F. Caskey

Journal of Medical Imaging, Vol. 10, Issue 05, 055001, (September 2023) https://doi.org/10.1117/1.JMI.10.5.055001

TOPICS: Skull, Simulations, Computed tomography, Magnetic resonance imaging, Acoustics, Transducers, Education and training, Ultrasonography, Aberration correction, Bone

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Image Perception, Observer Performance, and Technology Assessment

Approximating the Hotelling observer with autoencoder-learned efficient channels for binary signal detection tasks

Jason Granstedt, Weimin Zhou, Mark Anastasio

Journal of Medical Imaging, Vol. 10, Issue 05, 055501, (September 2023) https://doi.org/10.1117/1.JMI.10.5.055501

TOPICS: Education and training, Signal detection, Imaging systems, Signal attenuation, Breast, Binary data, Information operations, Image restoration, Image processing, Signal processing

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Purpose

The objective assessment of image quality (IQ) has been advocated for the analysis and optimization of medical imaging systems. One method of computing such IQ metrics is through a numerical observer. The Hotelling observer (HO) is the optimal linear observer, but conventional methods for obtaining the HO can become intractable due to large image sizes or insufficient data. Channelized methods are sometimes employed in such circumstances to approximate the HO. The performance of such channelized methods varies, with different methods obtaining superior performance to others depending on the imaging conditions and detection task. A channelized HO method using an AE is presented and implemented across several tasks to characterize its performance.

Approach

The process for training an AE is demonstrated to be equivalent to developing a set of channels for approximating the HO. The efficiency of the learned AE-channels is increased by modifying the conventional AE loss function to incorporate task-relevant information. Multiple binary detection tasks involving lumpy and breast phantom backgrounds across varying dataset sizes are considered to evaluate the performance of the proposed method and compare to current state-of-the-art channelized methods. Additionally, the ability of the channelized methods to generalize to images outside of the training dataset is investigated.

Results

AE-learned channels are demonstrated to have comparable performance with other state-of-the-art channel methods in the detection studies and superior performance in the generalization studies. Incorporating a cleaner estimate of the signal for the detection task is also demonstrated to significantly improve the performance of the proposed method, particularly in datasets with fewer images.

Conclusions

AEs are demonstrated to be capable of learning efficient channels for the HO. The resulting significant increase in detection performance for small dataset sizes when incorporating a signal prior holds promising implications for future assessments of imaging technologies.

Biomedical Applications in Molecular, Structural, and Functional Imaging

Neodymium acetate as a contrast agent for X-ray phase-contrast tomography

Jakob Reichmann, Torben Ruhwedel, Wiebke Möbius, Tim Salditt

Journal of Medical Imaging, Vol. 10, Issue 05, 056001, (October 2023) https://doi.org/10.1117/1.JMI.10.5.056001

TOPICS: X-rays, Neodymium, Tomography, Contrast agents, Tissues, Biological samples, Retina, Eye, Synchrotrons, Absorption

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Purpose

X-ray phase-contrast tomography (XPCT) is a non-destructive, three-dimensional imaging modality that provides higher contrast in soft tissue than absorption-based CT and allows one to cover the cytoarchitecture from the centi- and millimeter scale down to the nanoscale. To further increase contrast and resolution of XPCT, for example, in view of addressing connectivity issues in the central nervous system (CNS), metal staining is indispensable. However, currently used protocols, for example, based on osmium and/or uranium are less suited for XPCT, due to an excessive β / δ-ratio. In this work, we explore the suitability of different staining agents for XPCT. Particularly, neodymium(III)-acetate (NdAc), which has recently been proposed as a non-toxic, non-radioactive easy to use alternative contrast agent for uranyl acetate (UAc) in electron microscopy, is investigated. Due to its vertical proximity to UAc in the periodic table, similar chemical but better suited optical properties for phase contrast can be expected.

Approach

Differently stained whole eye samples of wild type mouse and tissues of the CNS are embedded into EPON epoxy resin and scanned using synchrotron as well as with laboratory radiation. Phase retrieval is performed on the projection images, followed by tomographic reconstruction, which enables a quantitative analysis based on the reconstructed electron densities. Segmentation techniques and rendering software is used to visualize structures of interest in the sample.

Results

We show that staining neuronal samples with NdAc enhances contrast, in particular for laboratory scans, allowing high-resolution imaging of biological soft tissue in-house. For the example of murine retina, specifically rods and cones as well as the sclera and the Ganglion cell layer seem to be targeted by the stain. A comparison of electron density by the evaluation of histograms allowed to determine quantitative measures to describe the difference between the examined stains.

Conclusion

The results suggest NdAc to be an effective stain for XPCT, with a preferential binding to anionic groups, such as phosphate and carboxyl groups at cell surfaces, targeting certain layers of the retina with a stronger selectivity compared to other staining agents. Due to the advantageous X-ray optical properties, the stain seems particularly well-suited for phase contrast, with a comparably small number density and an overall superior image quality at laboratory sources.

Low-dose quantitative CT myocardial flow measurement using a single volume scan: phantom and animal validation

Logan Hubbard, Sabee Molloi

Journal of Medical Imaging, Vol. 10, Issue 05, 056002, (October 2023) https://doi.org/10.1117/1.JMI.10.5.056002

TOPICS: Computed tomography, Animals, Tissues, Microspheres, Blood, Attenuation, Animal model studies, Data modeling, Myocardium, Arteries

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